Echocardiogram view classification using edge filtered scale-invariant motion features

ABSTRACT

According to one embodiment of the present invention, a method for echocardiogram view classification is provided. According to one embodiment of the present invention, a method comprises: obtaining a plurality of video images of a subject; aligning the plurality images; using the aligned images to generate a motion magnitude image; filtering the motion magnitude image using an edge map on image intensity; detecting features on the motion magnitude image, retaining only those features which lie in the neighborhood of intensity edges; encoding the remaining features by generating, x, y image coordinates, a motion magnitude histogram in a window around the feature point, and a histogram of intensity values near the feature point; and using the encoded features to classify the video images of the subject into a predetermined classification.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.12/819,183, filed Jun. 19, 2010, the disclosure of which is incorporatedby reference herein in its entirety.

BACKGROUND

The present invention relates to image classification problems, and morespecifically, to techniques for classifying echocardiogram videos.

Echocardiography is an important diagnostic aid in cardiology for themorphological and functional assessment of the heart. During anechocardiogram exam, a sonographer images the heart using ultrasound byplacing a transducer against the patient's chest. Reflected sound wavesreveal the inner structure of the heart walls and the velocities ofblood flows. Since these measurements are typically made using 2D slicesof the heart, the transducer position is varied during an echo exam tocapture different anatomical sections of the heart from differentviewpoints.

In current clinical practice, transducer positioning and viewpointcapture requires manual intervention in both imaging and ininterpretation. The sonographer manually delineates major anatomicalstructures like Left Ventricle (LV) and computes numerical quantitieslike ejection fraction from the images. This data is examined further bya cardiologist who makes the diagnosis based on the interpretation madefrom the echocardiogram. The knowledge of the probe viewpoint plays acrucial role in the interpretation process as it tells the examiner whatexactly is he or she looking at.

SUMMARY

According to one embodiment of the present invention, a methodcomprises: obtaining a plurality of video images of a subject; aligningthe plurality images; using the aligned images to generate a motionmagnitude image; filtering the motion magnitude image using an edge mapon image intensity; detecting features on the motion magnitude image,retaining only those features which lie in the neighborhood of intensityedges; encoding the remaining features by generating, x, y imagecoordinates, a motion magnitude histogram in a window around the featurepoint, and a histogram of intensity values near the feature point; andusing the encoded features to classify the video images of the subjectinto a predetermined classification.

According to one embodiment of the present invention, a method ofclassifying at least one echocardiogram video including a plurality ofecho images comprises: detecting an edge map of at least one of the echoimages; modifying at least one of the echo images to produce an edgefiltered motion magnitude image; locating the features at scaleinvariant points in the edge filtered motion magnitude image; andencoding the edge filtered motion magnitude image by using localinformation about the image at the scale invariant point locations.

According to another embodiment of the present invention, a system isprovided for processing a plurality of video images of a subjectcomprising: a processor for: aligning the plurality images; using thealigned images to generate a motion magnitude image; filtering themotion magnitude image using an edge map on image intensity; detectingfeatures on the motion magnitude image, retaining only those featureswhich lie in the neighborhood of intensity edges; encoding the remainingfeatures by generating, x, y image coordinates, a motion magnitudehistogram in a window around the feature point, and a histogram ofintensity values near the feature point; and using the encoded featuresto classify the video images of the subject into a predeterminedclassification.

According to another embodiment of the present invention, a computerprogram product for echocardiogram view classification comprises: acomputer usable medium having computer usable program code embodiedtherewith, the computer usable program code comprising: computer usableprogram code configured to: obtain a plurality of video images of asubject; align the plurality images; use the aligned images to generatea motion magnitude image; filter the motion magnitude image using anedge map on image intensity; detect features on the motion magnitudeimage, retaining only those features which lie in the neighborhood ofintensity edges; encode the remaining features by generating, x, y imagecoordinates, a motion magnitude histogram in a window around the featurepoint, and a histogram of intensity values near the feature point; anduse the encoded features to classify the video images of the subjectinto a predetermined classification.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 shows diagrams representing echocardiograms seen from differentviewpoints;

FIG. 2 shows diagrams representing echocardiograms seen from differentviewpoints and processed to show intensity, motion magnitude, and phasein accordance with an embodiment of the invention;

FIG. 3 shows a diagram representing an echocardiogram showing motionoverlaid with intensity in accordance with an embodiment of theinvention;

FIG. 4 shows diagrams representing echocardiograms processed to showedge maps, motion magnitude and edge map filtering in accordance with anembodiment of the invention;

FIG. 5 shows diagrams representing the processing of echocardiograms invarious stages in accordance with an embodiment of the invention;

FIG. 6 shows a view classification training process in accordance withan embodiment of the invention;

FIG. 7 shows a view classification process in accordance with anembodiment of the invention;

FIG. 8 shows a Table summarizing a database of echocardiogram videosused for experiments in accordance with an embodiment of the invention;

FIG. 9 shows a Table summarizing recognition rates for echocardiogramvideos during experiments in accordance with an embodiment of theinvention;

FIG. 10 shows a confusion matrix for eight-way view classification inaccordance with an embodiment of the invention;

FIG. 11 shows a flow chart of a method for echocardiogram viewclassification in accordance with an embodiment of the invention; and

FIG. 12 shows a high level block diagram of an information processingsystem useful for implementing one embodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the invention provide ways to improve the automaticclassification of viewpoints of echocardiogram videos. As discussedabove, classification of echocardiogram viewpoints is currentlygenerally done manually. In the last few years, there has beentremendous progress in the field of cardiac view recognition inechocardiograms and similarity search based decision support systems forcardiology. The primary focus in such systems is to be able toautomatically detect features from the echocardiogram video which canthen be used to conduct higher level disease discrimination andsimilarity search. Hence, the automatic echocardiogram viewclassification, the first step in any such system, has gainedimportance. Being primarily an image based classification problem, ithas attracted considerable attention from the computer vision andpattern recognition community.

Embodiments of the invention address the problem of automaticallyassigning view labels to echo videos obtained from unknown viewpoints.This problem is hard because even for videos belonging to sameviewpoint, significant variation arises from differences in pathologies,patients, instruments, and sonographers' expertise. The problem iscomplicated further by the fact that the images obtained byechocardiogram generally have high noise and low contrast. Furthermore,even for videos belonging to different viewpoints, its not trivial tolabel the viewpoints, and experts like cardiologists and cardiacsonographers are usually required to do this. In addition to this,obtained images can be translated, rotated or zoomed with respect toothers.

1. Introduction

A key component in any view classification system is one ofrepresentation—what feature set is used to represent a video? It iswell-known in the pattern recognition community, for example, that theproper choice of feature representation has a greater impact onperformance than selecting among the top network architectures.Embodiments of the invention employ a view classification approach thatuses a state-of-the-art classifier, vocabulary-based PMK and multiclassSVMs, and pairs it with a novel and powerful set of motion andedge-based features. In embodiments of the invention, interest pointsare scale-invariant points in the motion magnitude map that are alsonear intensity edges. This combination of motion and intensity featuresallows these embodiments to select the important portions of echocardiac anatomy to make the classification stage a success. The eightviewpoints classified by our system are shown in FIG. 1, which showssome of the more common viewpoints captured during an echocardiogramexam. In particular, these images in clockwise order from top left are:Apical Four Chamber (A4C), Parasternal Long Axis (PLA), ParasternalShort Axis-Basal (PSAB), Parasternal Short Axis-Papillary (PSAP), ApicalTwo Chambers (A2C), Apical Three Chambers (A3C), Apical Five Chambers(A5C), and Parasternal Short Axis-Mitral (PSAM).

2. Prior Systems

In an early first automatic cardiac view recognition system, Ebadollahiet al. in Ebadollahi, S. Chang, and H. Wu., “Automatic view recognitionin echocardiogram videos using parts-based representation. CVPR, pages2-9, 2004, proposed a constellation-of-parts based method. They used ageneric heart chamber detector (see D. R. Bailes, “The use of the graylevel sat to find the salient cavities in echocardiograms,” Journal ofVisual Communication and Image Representation, 7(2):169-195, 1996) tolocate heart chambers, and they represented the spatial arrangement ofthe chambers using a Markov Random Field (MRF) based relational graph.Final classification of a test image was performed using a SupportVector Machine on MRF network output. This method suffers fromsensitivity of the chamber detection method to frequently present noisein the echocardiogram images while demonstrating limited robustness tobasic image transformations.

Aschkenasy et al. represented each view by a signature obtained bymulti-resolution spline filtering of the training images. (See S.Aschkenasy, C. Jansen, R. Osterwalder, A. Linka, M. Unser, S. Marsch,and P. Hunziker, “Unsupervised image classification of medicalultrasound data by multiresolution elastic registration,” Ultrasound inMedicine and Biology, 32(7):1047-1054, 2006.) For a test image, thesetemplates were elastically deformed and the deformation energy, alongwith similarity, were used to classify the test image using a lineardiscriminant. Drawbacks of this method include the use of a classifierwith limited discrimination ability as well as the use of features whichare accurate only when the test image and template are close to oneanother.

Zhou et al. cast view recognition as a multi-class object detectionproblem. (See S. Zhou, J. Park, B. Georgescu, J. Simopoulos, J. Otsuki,and D. Comaniciu, “Image-based multiclass boosting and echocardiographicview classification,” CVPR, pages 1559-1565, 2006.) Using a multi-classLogitBoost network, this work exploited both positive examplescorresponding to viewpoint along with negatives corresponding tobackground. The use of Haar-like rectangular features, however, makesthe method sensitive to presence of noise in the images. Further, thelarge number of Haar-like features necessitated pruning and highrecognition rates were only guaranteed when sophisticated methods wereincluded to handle contradicting recognition results. Results for onlytwo-view classification were presented in this paper.

Otey et al. proposed a hierarchical classification strategy for viewclassification where first a classification into corresponding view type(e.g. Apical, Parasternal, etc.) was made, followed by a final viewclassification within the type. (See M. Otey, J. Bi, S. Krishna, B. Rao,J. Stoeckel, A. S. Katz, J. Han, and S. Parthasarathy, “Automatic viewrecognition for cardiac ultrasound images,” In MICCAI: Intl Workshop onComputer Vision for Intravascular and Intracardiac Imaging, pages187-194, 2006.) Features included gradient, peak, raw pixels and otherstatistical features, which were then fed to dimensionality reductionstage. The final classification was made using Logistic Model Treeclassifier at both levels.

Park et al. revisited boosting for view classification, where they usedthe MLBoost learning algorithm along with multi-object detection andintegrated local-global features. (See J. Park, S. Zhou, C. Simopoulos,J. Otsuki, and D. Comaniciu, “Automatic cardiac view classification ofechocardiogram,” In ICCV, pages 1-8, 2007.) Their system was builtaround a Haar-like feature based Left Ventricle (LV) region detector,and each view was modeled according to the spatial layout of other heartchambers with respect to the LV region. In this system, test images wereclassified based on their spatial region layout with respect to thetemplate region layouts. View classification is made based on a keyframe from the given echo video, the end diastolic frame. This methodcannot be used to detect views in which LV region is absent.

Roy et al. proposed the use of simple intensity histograms for viewclassification. (See A. Roy, S. Sural, J. Mukherjee, and A. K. Majumdar,“State-based modeling and object extraction from echocardiogram video,”IEEE Transactions on Information Technology in Biomedicine,12(3):366-376, 2008.) They reasoned that as different regions andchambers are visible in different echo viewpoints, the intensitydistribution can help discriminate viewpoint. The final classificationwas made using a multilayer perceptron where the number of hidden layerunits was empirically chosen. The signature histogram for a given echoimage is heavily dependent on the region of interest for which intensityvalues are considered and the choice of this region is not made explicitin this work.

Most recently, Beymer et al., for the first time, proposed to exploitthe motion information present in the echocardiogram videos for viewclassification. (See D. Beymer, T. Syeda-Mahmood, and F. Wang,“Exploiting spatio-temporal information for view recognition in cardiacecho videos,” IEEE Computer Society Workshop on Mathematical Methods inBiomedical Image Analysis (MMBIA), pages 1-8, 2008. They used ActiveShape Models (ASMs) to capture the shape and texture information andthen tracked these across different frames to derive motion information.All the information is concentrated by projecting it down to lowvariance eigenspaces and the final classification is done by minimizinga “sequence fit” measure. One of the downsides of this technique is thatASMs require manual delineation of shape in the training data, which canbe time consuming. This work also presented a comparative study of someof the competing view classification methods.

The view recognition problem can also be looked at as an objectrecognition problem if we identify each view as a different objectclass. It has been purported that intraview variation observed inechocardiogram videos is too complicated for generic object recognitionmethods to handle (See Beymer et al.).

As compared to previous work in echo view recognition, embodiments ofthe invention achieve higher recognition rates and is more extensible.Compared to the 4-class recognition experiments in Park et al. andBeymer et al., the present embodiments' 4-class recognition accuracy ishigher. Built on a scalable framework, the present embodiments do notrequire an initial LV detection stage as in Park et al., or an expensivemanual labeling during training as in Beymer et al. Also, the presentembodiments are the first to report good results on a larger 8-classviewpoint class experiment.

More generally, the present embodiments make an important contributionin its fusion of motion and intensity to form a discriminating“spatiotemporal” feature. As detailed in the following section, thefeatures employed are unique both in their location and description.Feature locations are scale invariant interest points in motionmagnitude that are also close to intensity edges. Feature descriptionsinclude position (x, y) and histograms of local motion and intensity.The utility of these features is borne out through a comparison with theSIFT/PMK experiment in Beymer et al.

A survey of object, activity and scene recognition literature revealsthat there have been attempts to use motion to define features but nonehas explored detecting and encoding features as we do. Jhuang et al.used a hierarchy of detectors for finding interest points, and one ofstages in the system uses features based on filtering of optical flow.(See H. Jhuang, T. Serre, L. Wolf, and T. Poggio, “A biologicallyinspired system for action recognition,” ICCV, 2007.) Sidenbladh andBlack used motion features obtained from the time derivative of wrappedconsecutive frames at multiple scales. (See H. Sidenbladh and M. J.Black, “Learning the statistics of people in images and video,” IJCV,54:54-1, 2003.)

Dalal et al. used oriented histogram of differential optical flow overthe entire image but did not use optical flow to detect any interestpoints (See N. Dalal, B. Triggs, and C. Schmid, “Human detection usingoriented histograms of flow and appearance,” ECCV, 2006.), while Laptevet al. (see I. Laptev, M. Marszaek, C. Schmid, and B. Rozenfeld,“Learning realistic human actions from movies,” CVPR, 2008) used thesame histograms but at points detected using the techniques taught in I.Laptev, “On space-time interest points,” IJCV, 2005. Efros et al. usedrectified and blurred optical flow over the whole image for humandetection, but motion is not used for interest point detection. (See A.A. Efros, A. C. Berg, G. Mori, and J. Malik, “Recognizing action at adistance,” ICCV, 2003.) Ke et al. used volumetric spatio-temporalfeatures for activity recognition. (See Y. Ke, R. Sukthankar, and M.Hebert, “Efficient visual event detection using volumetric features,”ICCV, 2005.) Dollar et al. used histograms of x and y components ofoptical flow for encoding features but the interest point detection wasdone using Quadrature Gabor Filters. (See P. Dollar, V. Rabaud, G.Cottrell, and S. Belongie, “Behavior recognition via sparsespatio-temporal features,” 2nd joint IEEE international workshop onvisual surveillance and performance evaluation of tracking andsurveillance,” 2005.) Using scale invariant features detected on theedge filtered motion magnitude field has the distinct advantage of beingable to locate anatomical features with significant motion, which theseabove methods lack. Further, since we use the histogram of motionmagnitude to encode our feature vectors, locating them where motion isinteresting makes sense.

3. Modeling Viewpoint Using Edge-filtered Motion Features

Since the native form of the data obtained from echocardiogram is avideo of anatomical structures in motion, we ideally seek a model whichexploits all the information (structural, textural and motion) presentin video for viewpoint discrimination and is not limited to using a fewkey frames. Further, we want a method which can be seamlessly applied toany viewpoint and is not limited to any particular subset of viewpoints(like in Park et al.), and thus our technique should be independent ofthe presence of specific anatomical structures in the images. Andfinally, our technique should provide recognition rates which arecompetitive with respect to the existing state-of-the-art.

In order to satisfy these conditions, the present embodiments utilize aframe-work which works with a few salient features obtained fromanalysis of both intensity frames (structural and textural information)and optical flow (motion information) in a given video sequence. Belowwe describe the basic preprocessing and the two important aspects ofsalient feature selection process—localization and encoding.

In accordance with embodiments of the invention, echocardiogram videosundergo some basic preprocessing before we begin the process of featurepoint localization and encoding. This includes extraction of the fansector (which contains the actual image) and a rough alignment. Forextraction of the region of interest, either manual or template matchingbased automated technique can be used (or the method described in Oteyet al.). Once the fan sector has been extracted, using the top, left andright extreme points, we automatically align all the echocardiogramvideos with each other via an affine transform. The three pointsmentioned above are sufficient to compute the affine transformationmatrix. Even though the classifier that we intend to use, PMK based SVM,is capable to handling small amount of image transformationaldiscrepancies, this initial alignment improves the discriminationability of our system.

In a given echocardiogram video of a heart cycle, there are imageregions (corresponding to different anatomical structures) whichdemonstrate significant amount of motion and other regions which do not.Furthermore, these regions are disparate for different viewpoints whilesimilar for image sequences belonging to same viewpoints. Tocharacterize this information, we analyzed the optical flow forechocardiogram video sequences computed using Demons algorithm (See A.Guimond, A. Roche, N. Ayache, and J. Meunier, “Three-dimensionalmultimodal brain warping using the demons algorithm and adaptiveintensity corrections,” IEEE Trans. on Medical Imaging, 20(1):58-69,2001).

Referring now to FIG. 2, the results of these computations are shown. Inparticular, the images in the first column of FIG. 2 show intensityimages, the second and third columns show motion magnitude and phasefrom optical flow computed between the first column frames and the nextvideo frame. The first two rows are Apical Four Chamber view while thelast two are Parasternal Long Axis. For motion magnitude and phaseimages, brighter regions represent higher values. Intraclass similarityand interclass disparity can be readily noted in the motion magnitudeimages. All four rows belong to different patients. There are twoimportant things to be noticed about the optical flow obtained for theechocardiogram image sequences shown in FIG. 2: 1) the deformation fieldcontains a considerable amount of noisy motion (even after smoothing) asan artifact of the noise present in the intensity images, and 2) of thetwo components of the motion field—magnitude and phase, phase issensitive to image transformations (rotation, translation etc) whilemagnitude is comparatively more stable.

Choosing features on motion magnitude alone would select a number ofweak features that follow erroneous motion and noise. Motion inechocardiogram images is meaningful only when it is associated withanatomical structures, and this information is absent in the motionmagnitude images. This is shown in FIG. 3, where intensity image hasbeen overlaid over the corresponding motion magnitude image. Inparticular, in FIG. 3, images from the top row, first two columns ofFIG. 2 were overlaid (intensity is shown by the narrow vertical brightregion just to the left of the center, by the bright region at the topcenter, and by the generally semicircular bright region extending fromthe two o-clock position and curving around to the seven o'clockposition, while the remaining bright regions indicate motion).Significant motion (for example, as shown by the oblong blobs, one atthe lower center and the other to the left of it) in the motionmagnitude image corresponds to anatomical features like heart valves,while extraneous motion is localized to noise infested “blank” regionse.g. heart chambers.

Embodiments of the present invention use the structural informationpresent in the intensity images to guide the feature localizationprocess. To achieve this, the embodiments filter the motion magnitudeimages using an edge map on image intensity. Thus, only motion whichcorresponds to anatomical structures is retained while the remainingextraneous motion is disregarded.

Given these edge-filtered motion maps, the next step is to choosespecific interest points. In the field of object recognition, much workexists on locating interest points (e.g. space time features (See I.Laptev, “On space-time interest points,” IJCV, 2005.), scale-invariantfeatures (See D. G. Lowe, “Distinctive image features fromscale-invariant key points,” IJCV, 60(2):91-110, 2004), etc). For thepresent embodiments we have chosen to use scale-invariant featuresprimarily due to their simplicity and effectiveness. It should be notedthat a direct application of these object recognition methods toechocardiogram images is largely ineffectual (as demonstrated by Beymeret al.) primarily due to low contrast and noise in echocardiogramimages. To the best of our knowledge, the present embodiments are thefirst to exploit edge filtered motion magnitude images for obtainingdiscriminating features in either echocardiogram viewpoint or objectrecognition literature.

Filtering the motion magnitude image using the edge map means thatmotion magnitude information only in the neighborhood of intensity edgesis retained. As scale invariant features (See D. G. Lowe) are sensitiveto edges in the image, we avoid features arising from artificial edgesby first detecting features on the motion magnitude image and thenretaining only those which lie in some neighborhood of the intensityedges. This process is demonstrated in FIG. 4, where the top row showsthe echocardiogram frame and its edge map. The second row shows themotion magnitude corresponding to frame in the top row with detectedfeature points depicted as individual bright points. The bottom rowshows the features filtered using the edge map. Note that, this processis not same as a mere sampling of the edges because the features pointsthat we retain correspond to significant motion, and we will use thiscrucial information when we encode the features.

Once the features have been located, the next important step is toencode them using information which will be useful in discrimination.Foremost, the location itself is important information, so we want toinclude the (x, y) image coordinates of the feature in our description.

Next, in order to account for all important motion information, weinclude a histogram of motion magnitude in a window around the featurepoint in our description. Here we leave out the phase informationbecause it is sensitive to common image transformations like rotation.The advantage of including motion magnitude information is that it canencode a certain amount of anatomical information (e.g. feature pointsaround heart valves would have a motion magnitude histogram skewedtowards higher values).

The structural information present in the intensity images is alsoimportant and we include it using a histogram of the intensity values ina neighborhood around the features point. Using histograms of bothmotion and texture information brings in robustness to possible presenceof outliers in the actual values of texture and motion magnitude.

Note that the scale invariant features (SIFT) (as described in D. G.Lowe, “Distinctive image features from scale-invariant keypoints,” IJCV,60(2):91-110, 2004.) also includes a method for feature descriptionusing oriented histograms of image gradients, but these are found to bein effectual for echocardiogram images (as gradients are too noisy). Thepresent embodiments outperforms SIFT descriptors by a considerablemargin, as described below in Section 5. The overall feature selectionand description framework in accordance with an embodiment is presentedin FIG. 5. As shown in FIG. 5, the feature location and descriptionprocess according to an embodiment of the invention is shown. The framesof the training videos undergo affine alignment and then optical flowfor each video is computed. Scale invariant features are detected fromthe magnitude of the optical flow and only those feature points whichlie in vicinity of the intensity image edge are retained. The featuresare finally encoded using the location, local texture histogram andlocal motion magnitude histogram.

4. Training and Testing Algorithms

Once the salient features have been detected and encoded, an effectiveclassification technique is required for viewpoint discrimination. Mostof the existing methods use a single key frame from the echocardiogramvideo sequence for classification purpose while the embodiments achievebetter performance by using more information than is present in thevideo sequence. The classification framework of embodiments of theinvention uses as many frames per video sequence as desired. We classifyeach frame independently and each frame casts a vote towards a parentvideo classification. A given video sequence is assigned a class, whichgets the maximum votes from the constituent frames. In case of a tie,re-classification is done only among tied classes. Empirically, we havenoted that classifying the video randomly is equally effective, becausethe number of cases with ties are rare.

One advantage of this technique is that the crucial problem of key frameselection is resolved, as the frames we use are obtained by uniformlysampling the video sequence. Further, using multiple frames per videobrings in some robustness to the classification process asmisclassification by a few outlier frames is automatically discounted.

The training process in embodiments of the invention detects and encodessalient features for each frame in the training data. See Algorithm 1shown in FIG. 6. Then, a hierarchical dictionary is learned from all thefeatures in the system using non-uniform bins. This may employ thetechniques described in K. Grauman and T. Darrell, “Approximatecorrespondences in high dimensions,” NIPS, 2006. Next, the dictionary isused to learn the model parameters of a kernel-based SVM, which mayemploy the techniques taught in Grauman et al. A testing process detectsand encodes the salient features in the given test video sequence in asimilar manner as the training algorithm. In one embodiment this testingprocess uses Algorithm 2, as shown in FIG. 7. Then, using the learneddictionary and SVM, each frame is individually classified and finalclassification is made using the voting scheme described earlier.

Like any other learning based method, there are a few parameters thatneed to be set in the system. Here, we provide some meaningfulheuristics that can be used to set these parameters. Foremost is thenumber of frames per video to be used for classification. We havenoticed that as the number of frames increases so does the recognitionrate, but at the expense of computation time, so this parameter shouldbe set based on accuracy-efficiency trade-off. Next is the neighborhoodsize selection for edge filtering, motion and texture histogramming.Here we have noticed that a neighborhood size of around 10% of the ROI(rectangle containing image sector/fan) size provides the best result.This number is also used to set the number of bins in histograms.

Minor changes in this size does not have any significant impact onrecognition rates. Parameters of scale invariant features detector areset to give about 200 features per frame. The next parameter is thedictionary size used during the learning phase. We set it such that 5%of the total features are retained in the dictionary with randominitialization. Finally, each component of the feature vector isuniformly weighted during dictionary creation.

5. Experiments

In order evaluate the performance of the view classification of theembodiments of the invention, we present results from two sets ofexperiments. First, in order to compare the performance of the presentembodiments with existing state-of-the-art techniques, we presentclassification results using A4C, PLA, PLAB and PLAP view points (theseare the same as those used in Beymer, et al.). Second, to demonstratethe capability of the embodiments to easily expand to classify more thanjust four views, we present results for a much larger and complicatedeight-way viewpoint classification problem.

We conducted our experiments on a large collection of echocardiogramvideos which contains 113 echocardiogram video sequences belonging toeight different viewpoints. Details of the database are listed in Table1, shown in FIG. 8. The videos were captured at 320.times.240 pixel sizeat 25 Hz. The ECG waveform included in the video was used to extract aheart cycle synchronized at the R-wave peak. These videos were manuallylabeled as belonging to one of the eight views.

For the first experiment, we implemented the setup described in Beymeret al. We used four viewpoints from the data set and conducted trainingand testing in a leave-one-out fashion. The experiment was repeated 20times with each time a different random initialization of the featuredictionary. Average recognition rates are reported in Table 2, shown inFIG. 9, where each row contains results using the method cited next tothe method name. Results for the competing methods were taken fromBeymer et al. The best result in each column is highlighted in bold. Themethod of the present embodiments was run with 20 frames per video andneighborhood size of 15.times.15 pixels with 15 bin histograms. Thedictionary was set to have approximately 14000 features (using theheuristic mentioned earlier).

The second experiment included all the eight classes mentioned inTable 1. We conducted the training and testing in a leave-one-outfashion and repeated the experiment 20 times each with a differentrandom initialization of the dictionary. The confusion matrix for theviewpoint classes using our method, presented in FIG. 10, yields anaverage recognition rate of 81%. In particular, FIG. 10 shows theConfusion Matrix for eight-way view classification. Numbers are thefraction of videos classified. Recognition rate over all videos is81.0%. The method of the present embodiments was run with 20 frames pervideo and neighborhood size of 15.times.15 pixels with 15 binhistograms. The dictionary was set to have approximately 23000 features.This method can process a video with 20-30 frames in under 1 minute.

It can be noted from the results reported in Table 2 that embodiments ofthe invention outperform the known existing state-of-the-art methods bya convincing margin. We attribute this primarily to a better and morecomprehensive use of the information present in echo videos. Whencompared to the results presented in Beymer et al., besides the betterrecognition rates, a significant advantage of the present embodiments isthat time and effort consumed by manual labeling of the ASM features isnot required. This translates to seamless expansion of our method tomore view classes and larger training sets.

We have also presented in Table 2 a comparison to the classificationmethod presented in Park et al. The Park et al. method is built around aHaar-like feature based Left Ventricle (LV) detector which severelylimits its capability to effectively classify those views which lack LVregion. Note that results presented are from a re-implementation of themethod which uses Left Atrium region in place of LV for PSAB view andHaar wavelet local features (as in the original paper) for LV detection.The method of the present embodiments demonstrates better recognitionrates as well as capability to include more view classes (with orwithout LV) over this technique. Moreover, being a boosting basedmethod, Park et al. tends to only work well when very large amount oftraining data is provided.

Finally, we have compared our method to an otherwise quite effectiveobject recognition method presented in Grauman et al. Thisimplementation used 25 images per view class and PCA to reduce thedimension of 128-long SIFT feature vectors to 2. Classification was doneusing PMK based SVM with 6-D feature vectors ((x, y) location, scale,orientation, 2 PCA coefficients). This comparison is particularlyimportant here because it demonstrates the importance of looking forgood features at the right place, in our case, motion magnitude images.SIFT features have been widely used in various object and imagerecognition application, but as demonstrated here, a direct applicationof SIFT based classification is ineffective.

From the results presented for the second experiment, shown in FIG. 10,it can be noted that even when the problem of view classification iscomplicated by presence of multiple similar looking classes, the methodof the present embodiments can still yield good results. It can be notedthat the 3 new Apical views create confusion with A4C view while PSAMcreates confusion with the other Parasternal views. Recognition rateover all videos is 81.0%.

FIG. 11 shows a flowchart of a method 10 for echocardiogram viewclassification in accordance with an embodiment of the invention. Instep 12, a plurality of video images of a subject are obtained. Theimages are then aligned in step 14. In step 16, the aligned images areused to generate a motion magnitude image. The motion magnitude image isthen filtered using an edge map on image intensity, in step 18. In step20, features are detected on the motion magnitude image such that onlythose features which lie in the neighborhood of intensity edges areretained. In step 22, the remaining features are encoded by generating:x, y image coordinates; a motion magnitude histogram in a window aroundthe feature point; and a histogram of intensity values near the featurepoint. The encoded features are used to classify the video images of thesubject into a predetermined classification.

Embodiments of the invention introduce a novel scalable system forechocardiogram viewpoint classification which uses scale invariantfeatures detected on edge filtered motion magnitude images and PMK basedSVM. Through experiment on real data we have demonstrated the methodconvincingly outperforms existing state-of-the-art methods for echo viewclassification. We have also presented results for a more difficulteight-way view classification problem.

As can be seen from the above disclosure, embodiments of the inventionprovide techniques for echocardiogram view classification. As will beappreciated by one skilled in the art, aspects of the present inventionmay be embodied as a system, method or computer program product.Accordingly, aspects of the present invention may take the form of anentirely hardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,aspects of the present invention may take the form of a computer programproduct embodied in one or more computer readable medium(s) havingcomputer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction running system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction running system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may run entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which run via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which run on the computeror other programmable apparatus provide processes for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be run substantially concurrently, or theblocks may sometimes be run in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

FIG. 12 is a high level block diagram showing an information processingsystem useful for implementing one embodiment of the present invention.The computer system includes one or more processors, such as processor102. The processor 102 is connected to a communication infrastructure104 (e.g., a communications bus, cross-over bar, or network). Varioussoftware embodiments are described in terms of this exemplary computersystem. After reading this description, it will become apparent to aperson of ordinary skill in the relevant art(s) how to implement theinvention using other computer systems and/or computer architectures.

The computer system can include a display interface 106 that forwardsgraphics, text, and other data from the communication infrastructure 104(or from a frame buffer not shown) for display on a display unit 108.The computer system also includes a main memory 110, preferably randomaccess memory (RAM), and may also include a secondary memory 112. Thesecondary memory 112 may include, for example, a hard disk drive 114and/or a removable storage drive 116, representing, for example, afloppy disk drive, a magnetic tape drive, or an optical disk drive. Theremovable storage drive 116 reads from and/or writes to a removablestorage unit 118 in a manner well known to those having ordinary skillin the art. Removable storage unit 118 represents, for example, a floppydisk, a compact disc, a magnetic tape, or an optical disk, etc. which isread by and written to by removable storage drive 116. As will beappreciated, the removable storage unit 118 includes a computer readablemedium having stored therein computer software and/or data.

In alternative embodiments, the secondary memory 112 may include othersimilar means for allowing computer programs or other instructions to beloaded into the computer system. Such means may include, for example, aremovable storage unit 120 and an interface 122. Examples of such meansmay include a program package and package interface (such as that foundin video game devices), a removable memory chip (such as an EPROM, orPROM) and associated socket, and other removable storage units 120 andinterfaces 122 which allow software and data to be transferred from theremovable storage unit 120 to the computer system.

The computer system may also include a communications interface 124.Communications interface 124 allows software and data to be transferredbetween the computer system and external devices. Examples ofcommunications interface 124 may include a modem, a network interface(such as an Ethernet card), a communications port, or a PCMCIA slot andcard, etc. Software and data transferred via communications interface124 are in the form of signals which may be, for example, electronic,electromagnetic, optical, or other signals capable of being received bycommunications interface 124. These signals are provided tocommunications interface 124 via a communications path (i.e., channel)126. This communications path 126 carries signals and may be implementedusing wire or cable, fiber optics, a phone line, a cellular phone link,an RF link, and/or other communications channels.

In this document, the terms “computer program medium,” “computer usablemedium,” and “computer readable medium” are used to generally refer tomedia such as main memory 110 and secondary memory 112, removablestorage drive 116, and a hard disk installed in hard disk drive 114.

Computer programs (also called computer control logic) are stored inmain memory 110 and/or secondary memory 112. Computer programs may alsobe received via communications interface 124. Such computer programs,when run, enable the computer system to perform the features of thepresent invention as discussed herein. In particular, the computerprograms, when run, enable the processor 102 to perform the features ofthe computer system. Accordingly, such computer programs representcontrollers of the computer system.

From the above description, it can be seen that the present inventionprovides a system, computer program product, and method for implementingthe embodiments of the invention. References in the claims to an elementin the singular is not intended to mean “one and only” unless explicitlyso stated, but rather “one or more.” All structural and functionalequivalents to the elements of the above-described exemplary embodimentthat are currently known or later come to be known to those of ordinaryskill in the art are intended to be encompassed by the present claims.No claim element herein is to be construed under the provisions of 35U.S.C. section 112, sixth paragraph, unless the element is expresslyrecited using the phrase “means for” or “step for.”

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A method comprising: obtaining a plurality of video images of asubject; aligning the plurality of images; using the aligned images togenerate a motion magnitude image; filtering the motion magnitude imageusing an edge map on image intensity; detecting features on the motionmagnitude image, retaining only those features which lie in theneighborhood of intensity edges; encoding the remaining features bygenerating, x, y image coordinates, a motion magnitude histogram in awindow around the feature point, and a histogram of intensity valuesnear the feature point; and using the encoded features to classify thevideo images of the subject into a predetermined classification.
 2. Themethod of claim 1 wherein said classifying comprises using avocabulary-based Pyramid Matching Kernel based Support Vector Machine.3. The method of claim 1 wherein the aligning comprises using affinetransformation.
 4. The method of claim 1 wherein motion magnitude imageis generated using Demons algorithm.
 5. The method of claim 1 whereinsaid video images are echocardiograms.
 6. A method of classifying atleast one echocardiogram video comprising: representing each image fromthe echocardiogram video by a set of salient features; modifying theimage to produce an edge filtered motion magnitude image; locating thefeatures at scale invariant points in the edge filtered motion magnitudeimage; and encoding the edge filtered motion magnitude image by usinglocal information about the image.
 7. The method of claim 6 wherein theencoding comprises encoding the edge filtered motion magnitude image byusing spatial information about the image.
 8. The method of claim 6wherein the encoding comprises encoding the edge filtered motionmagnitude image by using textual information about the image.
 9. Themethod of claim 6 wherein the encoding comprises encoding the edgefiltered motion magnitude image by using kinetic information about theimage.
 10. The method of claim 6 wherein the locating comprisesidentifying the scale invariant interest points in motion magnitude thatare also close to intensity edges in the edge filtered motion magnitudeimage.
 11. The method of claim 6 wherein the representing comprisesrepresenting the image by at least one position (x,y).
 12. The method ofclaim 6 wherein the representing comprises representing the image by atleast one histogram of local motion magnitude.
 13. The method of claim 6wherein the representing comprises representing the image by at leastone histogram of local intensity.
 14. The method of claim 6 wherein therepresenting comprises representing the image by at least one histogramof local texture.
 15. The method of claim 6 further comprisingclassifying the image into one of a set of predeterminedclassifications.
 16. The method of claim 15 wherein said classifyingcomprises using a vocabulary-based Pyramid Matching Kernel based SupportVector Machine.